Variance inflation is not the same as variance overestimation

Revision: Variance Inflation in Regression

Variance Inflation Factors (VIFs) are reexamined as conditioning diagnostics for models with intercept, with and without centering regressors to their means as oft debated. Conventional VIFs, both centered and uncentered, are flawed. To rectify matters, two types of orthogonality are noted: vector-space orthogonality and uncorrelated centered regressors.The key to our approach lies in feasible ...

Total Variance as an Exact Analysis of the Sample Variance ∗

Given a sequence of fractional frequency deviates, we investigate the relationship between the sample variance of these deviates and the total variance (Totvar) estimator of the Allan variance. We demonstrate that we can recover exactly twice the sample variance by renormalizing the Totvar estimator and then summing it over dyadic averaging times 1, 2, 4, . . . , 2 along with one additional ter...

Inferences on the Generalized Variance under Normality

Generalized variance is applied for determination of dispersion in a multivariate population and is a successful measure for concentration of multivariate data. In this article, we consider constructing confidence interval and testing the hypotheses about generalized variance in a multivariate normal distribution and give a computational approach. Simulation studies are performed to compare thi...

Variance inflation factors in the analysis of complex survey data

Survey data are often used to fit linear regression models. The values of covariates used in modeling are not controlled as they might be in an experiment. Thus, collinearity among the covariates is an inevitable problem in the analysis of survey data. Although many books and articles have described the collinearity problem and proposed strategies to understand, assess and handle its presence, ...

Variance Minimization and the Theory of Inflation Hedging